Vaarwerk B, Breunis WB, Haveman LM, de Keizer B, Jehanno N, Borgwardt L, et al. Fluorine-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) computed tomography (CT) for the detection of bone, lung, and lymph node metastases in rhabdomyosarcoma. Cochrane Database Syst Rev. 2021;11(11):Cd012325.
Sari H, Mingels C, Alberts I, Hu J, Buesser D, Shah V, et al. First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur J Nucl Med Mol Imaging. 2022;49(6):1997–2009.
Article CAS PubMed Google Scholar
Moradi H, Vashistha R, O’Brien K, Hammond A, Vegh V, Reutens D. A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET. EJNMMI Res. 2024;14(1):1.
Article CAS PubMed PubMed Central Google Scholar
Khalil MM. Basics and advances of quantitative PET imaging. In: Khalil MM, editor. Basic science of PET imaging. Cham: Springer International Publishing; 2017. p. 303–22.
Sokoloff L, Reivich M, Kennedy C, Des Rosiers MH, Patlak CS, Pettigrew KD, et al. The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem. 1977;28(5):897–916.
Article CAS PubMed Google Scholar
Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol. 1979;6(5):371–88.
Article CAS PubMed Google Scholar
Wang G, Rahmim A, Gunn RN. PET parametric imaging: past, present, and future. IEEE Trans Radiat Plasma Med Sci. 2020;4(6):663–75.
Article PubMed PubMed Central Google Scholar
Takikawa S, Dhawan V, Spetsieris P, Robeson W, Chaly T, Dahl R, et al. Noninvasive quantitative fluorodeoxyglucose PET studies with an estimated input function derived from a population-based arterial blood curve. Radiology. 1993;188(1):131–6.
Article CAS PubMed Google Scholar
Feng D, Wong K-P, Wu C-M, Siu W-C. A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study. IEEE Trans Inf Technol Biomed. 1997;1(4):243–54.
Article CAS PubMed Google Scholar
Moradi H, Vegh V, Reutens D. Non-invasive input function extraction from dynamic PET using machine learning along with an iterative approach. J Nucl Med. 2021;62(supplement 1):1416.
Choi Y, Hawkins RA, Huang SC, Gambhir SS, Brunken RC, Phelps ME, et al. Parametric images of myocardial metabolic rate of glucose generated from dynamic cardiac PET and 2-[18F]fluoro-2-deoxy-d-glucose studies. J Nucl Med. 1991;32(4):733–8.
van der Weerdt AP, Klein LJ, Boellaard R, Visser CA, Visser FC, Lammertsma AA. Image-derived input functions for determination of MRGlu in cardiac (18)F-FDG PET scans. J Nucl Med. 2001;42(11):1622–9.
Lüdemann L, Sreenivasa G, Michel R, Rosner C, Plotkin M, Felix R, et al. Corrections of arterial input function for dynamic H215O PET to assess perfusion of pelvic tumours: arterial blood sampling versus image extraction. Phys Med Biol. 2006;51(11):2883–900.
Ohtake T, Kosaka N, Watanabe T, Yokoyama I, Moritan T, Masuo M, et al. Noninvasive method to obtain input function for measuring tissue glucose utilization of thoracic and abdominal organs. J Nucl Med. 1991;32(7):1432–8.
Zanotti-Fregonara P, Chen K, Liow JS, Fujita M, Innis RB. Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab. 2011;31(10):1986–98.
Article PubMed PubMed Central Google Scholar
Zanotti-Fregonara P, el Fadaili M, Maroy R, Comtat C, Souloumiac A, Jan S, et al. Comparison of eight methods for the estimation of the image-derived input function in dynamic [(18)F]-FDG PET human brain studies. J Cereb Blood Flow Metab. 2009;29(11):1825–35.
Feng T, Tsui BM, Li X, Vranesic M, Lodge MA, Gulaldi NC, et al. Image-derived and arterial blood sampled input functions for quantitative PET imaging of the angiotensin II subtype 1 receptor in the kidney. Med Phys. 2015;42(11):6736–44.
Article PubMed PubMed Central Google Scholar
Sari H, Erlandsson K, Law I, Larsson HBW, Ourselin S, Arridge S, et al. Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method. J Cereb Blood Flow Metab. 2016;37(4):1398–409.
Article PubMed PubMed Central Google Scholar
Khalighi MM, Deller TW, Fan AP, Gulaka PK, Shen B, Singh P, et al. Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping. J Cereb Blood Flow Metab. 2018;38(1):126–35.
Article CAS PubMed Google Scholar
Fung EK, Planeta-Wilson B, Mulnix T, Carson RE. A multimodal approach to image-derived input functions for brain PET. IEEE Nucl Sci Symp Conf Record Nucl Sci Symp. 2009;2009:2710–4.
Evans E, Buonincontri G, Izquierdo D, Methner C, Hawkes RC, Ansorge RE, et al. Combining MRI with PET for partial volume correction improves image-derived input functions in mice. IEEE Trans Nucl Sci. 2015;62(3 Pt 1):628–33.
Article PubMed PubMed Central Google Scholar
Vashistha R, Moradi H, Hammond A, O’Brien K, Rominger A, Sari H, et al. Non-invasive arterial input function estimation using an MRI atlas and machine learning. PREPRINT (Version 1) available at Research Square. 2023.
Fung EK, Carson RE. Cerebral blood flow with [15O] water PET studies using an image-derived input function and MR-defined carotid centerlines. Phys Med Biol. 2013;58(6):1903.
Article PubMed PubMed Central Google Scholar
Lyoo CH, Zanotti-Fregonara P, Zoghbi SS, Liow J-S, Xu R, Pike VW, et al. Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram. PLoS ONE. 2014;9(2):e89101.
Article PubMed PubMed Central Google Scholar
Islam MM, Tsujikawa T, Mori T, Kiyono Y, Okazawa H. Estimation of arterial input by a noninvasive image derived method in brain H215O PET study: confirmation of arterial location using MR angiography. Phys Med Biol. 2017;62(11):4514.
Article CAS PubMed Google Scholar
Okazawa H, Higashino Y, Tsujikawa T, Arishima H, Mori T, Kiyono Y, et al. Noninvasive method for measurement of cerebral blood flow using O-15 water PET/MRI with ASL correlation. Eur J Radiol. 2018;105:102–9.
Young P, Appel L, Tolf A, Kosmidis S, Burman J, Rieckmann A, et al. Image-derived input functions from dynamic 15O–water PET scans using penalised reconstruction. EJNMMI Phys. 2023;10(1):15.
Article PubMed PubMed Central Google Scholar
Zhang T, Wu S, Zhang X, Dai Y, Wang A, Zhang H, et al. Spatial normalization and quantification approaches of PET imaging for neurological disorders. Eur J Nucl Med Mol Imaging. 2022;49(11):3809–29.
Vashistha R, Moradi H, Hammond A, O’Brien K, Rominger A, Sari H, et al. ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages. EJNMMI Res. 2024;14(1):10.
Zanotti-Fregonara P, Maroy R, Comtat C, Jan S, Gaura V, Bar-Hen A, et al. Comparison of 3 methods of automated internal carotid segmentation in human brain PET studies: application to the estimation of arterial input function. J Nucl Med. 2009;50(3):461.
Volpi T, Maccioni L, Colpo M, Debiasi G, Capotosti A, Ciceri T, et al. An update on the use of image-derived input functions for human PET studies: new hopes or old illusions? EJNMMI Res. 2023;13(1):97.
Article PubMed PubMed Central Google Scholar
Liptrot M, Adams KH, Martiny L, Pinborg LH, Lonsdale MN, Olsen NV, et al. Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling. Neuroimage. 2004;21(2):483–93.
Zheng X, Tian G, Huang SC, Feng D. A hybrid clustering method for ROI delineation in small-animal dynamic PET images: application to the automatic estimation of FDG input functions. IEEE Trans Inf Technol Biomed. 2011;15(2):195–205.
Kuttner S, Wickstrøm KK, Kalda G, Dorraji SE, Martin-Armas M, Oteiza A, et al. Machine learning derived input-function in a dynamic 18F-FDG PET study of mice. Biomed Phys Eng Exp. 2020;6(1):015020.
Varnyú D, Szirmay-Kalos L (Eds) Blood input function estimation in positron emission tomography with deep learning. In: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC); 2021 16–23 Oct. 2021.
Prenosil GA, Sari H, Fürstner M, Afshar-Oromieh A, Shi K, Rominger A, et al. Performance characteristics of the biograph vision quadra PET/CT system with a long axial field of view using the NEMA NU 2–2018 standard. J Nucl Med. 2022;63(3):476–84.
Article CAS PubMed Google Scholar
Vandenberghe S, Moskal P, Karp JS. State of the art in total body PET. EJNMMI Phys. 2020;7(1):35.
Article PubMed PubMed Central Google Scholar
Percival DB, Walden AT. Wavelet methods for time series analysis. Cambridge: Cambridge University Press; 2000.
Nielsen F. Hierarchical clustering. In: Nielsen F, editor. Introduction to HPC with MPI for data science. Cham: Springer International Publishing; 2016. p. 195–211.
Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math. 1963;11(2):431–41.
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